ImaGene: a convolutional neural network to quantify natural selection from genomic data.

BACKGROUND: The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determining such genetic bases is an evolutionary framework. As sites targeted by natural selection are likely to harbor important functionalities for the carrier, the identification of selection signatures in the genome has the potential to unveil the genetic mechanisms underpinning human phenotypes. Popular methods of detecting such signals rely on compressing genomic information into summary statistics, resulting in the loss of information. Furthermore, few methods are able to quantify the strength of selection. Here we explored the use of deep learning in evolutionary biology and implemented a program, called ImaGene, to apply convolutional neural networks on population genomic data for the detection and quantification of natural selection. RESULTS: ImaGene enables genomic information from multiple individuals to be represented as abstract images. Each image is created by stacking aligned genomic data and encoding distinct alleles into separate colors. To detect and quantify signatures of positive selection, ImaGene implements a convolutional neural network which is trained using simulations. We show how the method implemented in ImaGene can be affected by data manipulation and learning strategies. In particular, we show how sorting images by row and column leads to accurate predictions. We also demonstrate how the misspecification of the correct demographic model for producing training data can influence the quantification of positive selection. We finally illustrate an approach to estimate the selection coefficient, a continuous variable, using multiclass classification techniques. CONCLUSIONS: While the use of deep learning in evolutionary genomics is in its infancy, here we demonstrated its potential to detect informative patterns from large-scale genomic data. We implemented methods to process genomic data for deep learning in a user-friendly program called ImaGene. The joint inference of the evolutionary history of mutations and their functional impact will facilitate mapping studies and provide novel insights into the molecular mechanisms associated with human phenotypes.

Multisite analysis of high-grade serous epithelial ovarian cancers identifies genomic regions of focal and recurrent copy number alteration in 3q26.2 and 8q24.3.

High-grade serous epithelial ovarian cancer (HGS-EOC) is a systemic disease, with marked intra and interpatient tumor heterogeneity. The issue of spatial and temporal heterogeneity has long been overlooked, hampering the possibility to identify those genomic alterations that persist, before and after therapy, in the genome of all tumor cells across the different anatomical districts. This knowledge is the first step to clarify those molecular determinants that characterize the tumor biology of HGS-EOC and their route toward malignancy. In our study, -omics data were generated from 79 snap frozen matched tumor biopsies, withdrawn before and after chemotherapy from 24 HGS-EOC patients, gathered together from independent cohorts. The landscape of somatic copy number alterations depicts a more homogenous and stable genomic portrait than the single nucleotide variant profile. Genomic identification of significant targets in cancer analysis identified two focal and minimal common regions (FMCRs) of amplification in the cytoband 3q26.2 (region α, 193 kb long) and 8q24.3 (region ß, 495 kb long). Analysis in two external databases confirmed regions α and ß are features of HGS-EOC. The MECOM gene is located in region α, and 15 genes are in region ß. No functional data are yet available for the genes in the ß region. In conclusion, we have identified for the first time two FMCRs of amplification in HGS-EOC, opening up a potential biological role in its etiopathogenesis.

Eps8 controls dendritic spine density and synaptic plasticity through its actin-capping activity.

Actin-based remodelling underlies spine structural changes occurring during synaptic plasticity, the process that constantly reshapes the circuitry of the adult brain in response to external stimuli, leading to learning and memory formation. A positive correlation exists between spine shape and synaptic strength and, consistently, abnormalities in spine number and morphology have been described in a number of neurological disorders. In the present study, we demonstrate that the actin-regulating protein, Eps8, is recruited to the spine head during chemically induced long-term potentiation in culture and that inhibition of its actin-capping activity impairs spine enlargement and plasticity. Accordingly, mice lacking Eps8 display immature spines, which are unable to undergo potentiation, and are impaired in cognitive functions. Additionally, we found that reduction in the levels of Eps8 occurs in brains of patients affected by autism compared to controls. Our data reveal the key role of Eps8 actin-capping activity in spine morphogenesis and plasticity and indicate that reductions in actin-capping proteins may characterize forms of intellectual disabilities associated with spine defects.

The Uromodulin Gene Locus Shows Evidence of Pathogen Adaptation through Human Evolution.

Common variants in the UMOD gene encoding uromodulin, associated with risk of hypertension and CKD in the general population, increase UMOD expression and urinary excretion of uromodulin, causing salt-sensitive hypertension and renal lesions. To determine the effect of selective pressure on variant frequency, we investigated the allelic frequency of the lead UMOD variant rs4293393 in 156 human populations, in eight ancient human genomes, and in primate genomes. The T allele of rs4293393, associated with CKD risk, has high frequency in most modern populations and was the one detected in primate genomes. In contrast, we identified only the derived, C allele in Denisovan and Neanderthal genomes. The distribution of the UMOD ancestral allele did not follow the ancestral susceptibility model observed for variants associated with salt-sensitive hypertension. Instead, the global frequencies of the UMOD alleles significantly correlated with pathogen diversity (bacteria, helminths) and prevalence of antibiotic-resistant urinary tract infections (UTIs). The inverse correlation found between urinary levels of uromodulin and markers of UTIs in the general population substantiates the link between UMOD variants and protection against UTIs. These data strongly suggest that the UMOD ancestral allele, driving higher urinary excretion of uromodulin, has been kept at a high frequency because of its protective effect against UTIs.

LSD1 Neurospecific Alternative Splicing Controls Neuronal Excitability in Mouse Models of Epilepsy.

Alternative splicing in the brain is dynamic and instrumental to adaptive changes in response to stimuli. Lysine-specific demethylase 1 (LSD1/KDM1A) is a ubiquitously expressed histone H3Lys4 demethylase that acts as a transcriptional co-repressor in complex with its molecular partners CoREST and HDAC1/2. In mammalian brain, alternative splicing of LSD1 mini-exon E8a gives rise to neuroLSD1, a neurospecific isoform that, upon phosphorylation, acts as a dominant-negative causing disassembly of the co-repressor complex and de-repression of target genes. Here we show that the LSD1/neuroLSD1 ratio changes in response to neuronal activation and such effect is mediated by neurospecific splicing factors NOVA1 and nSR100/SRRM4 together with a novel cis-silencer. Indeed, we found that, in response to epileptogenic stimuli, downregulation of NOVA1 reduces exon E8a splicing and expression of neuroLSD1. Using behavioral and EEG analyses we observed that neuroLSD1-specific null mice are hypoexcitable and display decreased seizure susceptibility. Conversely, in a mouse model of Rett syndrome characterized by hyperexcitability, we measured higher levels of NOVA1 protein and upregulation of neuroLSD1. In conclusion, we propose that, in the brain, correct ratio between LSD1 and neuroLSD1 contributes to excitability and, when altered, could represent a pathogenic event associated with neurological disorders involving altered E/I.

Epileptiform activity and cognitive deficits in SNAP-25(+/-) mice are normalized by antiepileptic drugs.

Synaptosomal-associated protein of 25 kDa (SNAP-25) is a protein that participates in the regulation of synaptic vesicle exocytosis through the formation of the soluble NSF attachment protein receptor complex and modulates voltage-gated calcium channels activity. The Snap25 gene has been associated with schizophrenia, attention deficit hyperactivity disorder, and bipolar disorder, and lower levels of SNAP-25 have been described in patients with schizophrenia. We used SNAP-25 heterozygous (SNAP-25(+/-)) mice to investigate at which extent the reduction of the protein levels affects neuronal network function and mouse behavior. As interactions of genotype with the specific laboratory conditions may impact behavioral results, the study was performed through a multilaboratory study in which behavioral tests were replicated in at least 2 of 3 distinct European laboratories. Reductions of SNAP-25 levels were associated with a moderate hyperactivity, which disappeared in the adult animals, and with impaired associative learning and memory. Electroencephalographic recordings revealed the occurrence of frequent spikes, suggesting a diffuse network hyperexcitability. Consistently, SNAP-25(+/-) mice displayed higher susceptibility to kainate-induced seizures, paralleled by degeneration of hilar neurons. Notably, both EEG profile and cognitive defects were improved by antiepileptic drugs. These results indicate that reduction of SNAP-25 expression is associated to generation of epileptiform discharges and cognitive dysfunctions, which can be effectively treated by antiepileptic drugs.

Structure and evolution of vertebrate aldehyde oxidases: from gene duplication to gene suppression.

Aldehyde oxidases (AOXs) and xanthine dehydrogenases (XDHs) belong to the family of molybdo-flavoenzymes. Although AOXs are not identifiable in fungi, these enzymes are represented in certain protists and the majority of plants and vertebrates. The physiological functions and substrates of AOXs are unknown. Nevertheless, AOXs are major drug metabolizing enzymes, oxidizing a wide range of aromatic aldehydes and heterocyclic compounds of medical/toxicological importance. Using genome sequencing data, we predict the structures of AOX genes and pseudogenes, reconstructing their evolution. Fishes are the most primitive organisms with an AOX gene (AOXα), originating from the duplication of an ancestral XDH. Further evolution of fishes resulted in the duplication of AOXα into AOXß and successive pseudogenization of AOXα. AOXß is maintained in amphibians and it is the likely precursors of reptilian, avian, and mammalian AOX1. Amphibian AOXγ is a duplication of AOXß and the likely ancestor of reptilian and avian AOX2, which, in turn, gave rise to mammalian AOX3L1. Subsequent gene duplications generated the two mammalian genes, AOX3 and AOX4. The evolution of mammalian AOX genes is dominated by pseudogenization and deletion events. Our analysis is relevant from a structural point of view, as it provides information on the residues characterizing the three domains of each mammalian AOX isoenzyme. We cloned the cDNAs encoding the AOX proteins of guinea pig and cynomolgus monkeys, two unique species as to the evolution of this enzyme family. We identify chimeric RNAs from the human AOX3 and AOX3L1 pseudogenes with potential to encode a novel microRNA.

Signatures of environmental genetic adaptation pinpoint pathogens as the main selective pressure through human evolution.

Previous genome-wide scans of positive natural selection in humans have identified a number of non-neutrally evolving genes that play important roles in skin pigmentation, metabolism, or immune function. Recent studies have also shown that a genome-wide pattern of local adaptation can be detected by identifying correlations between patterns of allele frequencies and environmental variables. Despite these observations, the degree to which natural selection is primarily driven by adaptation to local environments, and the role of pathogens or other ecological factors as selective agents, is still under debate. To address this issue, we correlated the spatial allele frequency distribution of a large sample of SNPs from 55 distinct human populations to a set of environmental factors that describe local geographical features such as climate, diet regimes, and pathogen loads. In concordance with previous studies, we detected a significant enrichment of genic SNPs, and particularly non-synonymous SNPs associated with local adaptation. Furthermore, we show that the diversity of the local pathogenic environment is the predominant driver of local adaptation, and that climate, at least as measured here, only plays a relatively minor role. While background demography by far makes the strongest contribution in explaining the genetic variance among populations, we detected about 100 genes which show an unexpectedly strong correlation between allele frequencies and pathogenic environment, after correcting for demography. Conversely, for diet regimes and climatic conditions, no genes show a similar correlation between the environmental factor and allele frequencies. This result is validated using low-coverage sequencing data for multiple populations. Among the loci targeted by pathogen-driven selection, we found an enrichment of genes associated to autoimmune diseases, such as celiac disease, type 1 diabetes, and multiples sclerosis, which lends credence to the hypothesis that some susceptibility alleles for autoimmune diseases may be maintained in human population due to past selective processes.

Linking coronary artery disease to neurodegenerative diseases through systems genetics.

Coronary artery disease (CAD) is still a leading cause of death worldwide despite the extensive research and the considerable progresses made through the years. As other cardiovascular diseases, CAD is the result of the complex interaction between genetic variants and environmental factors. Currently identified genetic loci associated to CAD revealed the contribution of multiple molecular pathways to its pathogenesis, suggesting the need for a systemic approach to understand the role of genetic determinants. In this study we wanted to investigate how GWAS variants associated to CAD interact with each other and with nearby genes in the context of the coronary artery molecular interactome. GWAS variants associated to CAD were selected from GWAS Catalog, then, a tissue-specific interactome was constructed integrating protein-protein interactions (PPI) from multiple public repositories and computationally inferred co-expression relationships. To focus on the part of the network most relevant for CAD, we selected the interactions connecting the genes carrying a variant associated to the disease. A functional enrichment analysis conducted on the subnetwork revealed that genes carrying genetic variants associated to CAD closely interact with genes related to relevant biological processes, such as extracellular matrix organization, lipoprotein clearance, arterial morphology and inflammatory response. These results confirm that the identified subnetwork reflects the molecular pathways altered in CAD and intercepted by the selected variants. Interestingly, the most connected nodes of the network included amyloid beta precursor protein (APP) and huntingtin (HTT), both implicated in neurodegenerative disorders. In recent years the interest in investigating the common processes between cardiovascular diseases and neurodegenerative disorders is increasing, with growing evidence of a link between CAD and Alzheimer's disease. The results obtained in this work support the association between such apparently unrelated diseases and highlight the necessity of a systems biology approach to better elucidate shared pathological mechanisms.

Lack of imbalance between the master regulators TTF1/NKX2-1 and ΔNp63/p40 implies adverse prognosis in non-small cell lung cancer.

The transcription factors TTF1/NKX2-1 and ΔNp63/p40 are the counterposed molecular markers associated with the main Non-Small Cell Lung Cancer subtypes: TTF1 for adenocarcinoma, p40 for squamous cell carcinoma. Although they generally display a mutually exclusive expression, some exceptions exist simultaneously lacking or (very rarely) expressing both markers, either pattern being associated to poor prognosis. Hence, we quantitatively analyzed the relationship between their coordinated activity and prognosis. By analyzing the respective downstream transcriptional programs of the two genes, we defined a simple quantitative index summarizing the amount of mutual exclusivity between their activities, called Mean Absolute Activity (MAA). Systematic analysis of the MAA index in a dataset of 1018 NSCLC samples replicated on a validation dataset of 275 showed that the loss of imbalance between TTF-1 and p40 corresponds to a steady, progressive reduction in both overall and recurrence-free survival. Coherently, samples correspondent to more balanced activities were enriched for pathways related to increased malignancy and invasiveness. Importantly, multivariate analysis showed that the prognostic significance of the proposed index MAA is independent of other clinical variables including stage, sex, age and smoke exposure. These results hold irrespectively of tumor morphology across NSCLC subtypes, providing a unifying description of different expression patterns.

Utility of KI-67 as a prognostic biomarker in pulmonary neuroendocrine neoplasms: a systematic review and meta-analysis.

OBJECTIVES: Ki-67, a marker of cellular proliferation, is associated with prognosis across a wide range of tumours, including gastroenteropancreatic neuroendocrine neoplasms (NENs), lymphoma, urothelial tumours and breast carcinomas. Its omission from the classification system of pulmonary NENs is controversial. This systematic review sought to assess whether Ki-67 is a prognostic biomarker in lung NENs and, if feasible, proceed to a meta-analysis. RESEARCH DESIGN AND METHODS: Medline (Ovid), Embase, Scopus and the Cochrane library were searched for studies published prior to 28 February 2019 and investigating the role of Ki-67 in lung NENs. Eligible studies were those that included more than 20 patients and provided details of survival outcomes, namely, HRs with CIs according to Ki-67 percentage. Studies not available as a full text or without an English manuscript were excluded. This study was prospectively registered with PROSPERO. RESULTS: Of 11 814 records identified, seven studies met the inclusion criteria. These retrospective studies provided data for 1268 patients (693 TC, 281 AC, 94 large cell neuroendocrine carcinomas and 190 small cell lung carcinomas) and a meta-analysis was carried out to estimate a pooled effect. Random effects analyses demonstrated an association between a high Ki-67 index and poorer overall survival (HR of 2.02, 95% CI 1.16 to 3.52) and recurrence-free survival (HR 1.42; 95% CI 1.01 to 2.00). CONCLUSION: This meta-analysis provides evidence that high Ki-67 labelling indices are associated with poor clinical outcomes for patients diagnosed with pulmonary NENs. This study is subject to inherent limitations, but it does provide valuable insights regarding the use of the biomarker Ki-67, in a rare tumour. PROSPERO REGISTRATION NUMBER: CRD42018093389.

Expression of mutant beta2 nicotinic receptors during development is crucial for epileptogenesis.

Autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE) is a focal form of epilepsy characterized by seizures occurring during non-REM sleep. We have developed and characterized the first mouse model for ADNFLE type III carrying the V287L mutation of the beta2 subunit of neuronal nicotinic receptor. Mice expressing mutant receptors show a spontaneous epileptic phenotype by electroencephalography with very frequent interictal spikes and seizures. Expression of the mutant beta2 subunit is driven by a neuronal-specific tetracycline-controlled promoter, which allows planned silencing of transgene expression in a reversible fashion and tracking the involvement of mutant receptor in crucial phases of epileptogenesis. We found that restricted silencing during development is sufficient to prevent the occurrence of epileptic seizures in adulthood. Our data indicate that mutant nicotinic receptors are responsible for abnormal formation of neuronal circuits and/or long-lasting alteration of network assembly in the developing brain, thus leading to epilepsy.

Trends in biomedical engineering: focus on Genomics and Proteomics.

Genomics is the study of an organism's genome aimed at the functional specification of the different parts of the sequence that comprise the blueprint of the living cell to unveil the mechanisms of the physiology of the cell and its basic, developmental, and tissue-specific processes. Proteomics is the comprehensive study of the executive molecules of the cell coded by the genome, further raising the level of complexity, because of the large amplification in the number, going from genes to proteins, and to the sophisticated structural and functional characterization of protein products, which confer specific biochemical properties. While continuous progress in technology provides new experimental solutions to study and measure the behavior of genes and proteins in the cell, the analysis and the management of biological data cannot be uncoupled from the use of mathematics, statistics, and informatics disciplines that play a key role in modern molecular biology.Together, genomics and proteomics, meant as complementary approaches, delineate the framework of modern molecular medicine, where the knowledge of the functional mechanisms on a subcellular scale, both under physiologic and pathologic conditions, may lead to an improvement in diagnosis, therapy, and drug development.

Clinical implications of lung neuroendocrine neoplasm classification.

INTRODUCTION: Neuroendocrine neoplasms of the lung (Lung NENs) encompass NE tumors (NETs), which are in turn split into typical and atypical carcinoids, and NE carcinomas (NECs), which group together small-cell carcinoma and large-cell NE carcinoma. This classification is the current basis for orienting the daily practice of these patients, with diagnostic, prognostic, and predictive inferences. AREAS COVERED: The clinical implications of lung NEN classification are addressed according to three converging perspectives, which were dissected through an extensive literature overview: (1) how to put intratumor heterogeneity into the context of the current classification; (2) how to contextualize immunohistochemistry markers to improve diagnosis, prognosis, and therapy prediction; and (3) how to use immuno-oncology strategies for life-threatening NECs, which still account for 90% or more of lung NENs. EXPERT OPINION: We provide practical insights to account for intratumor heterogeneity, practice the choice of immunohistochemistry markers, and emphasize once again the added value of immuno-oncology in the setting of personalized medicine of lung NENs.

Morphologic and molecular classification of lung neuroendocrine neoplasms.

Neuroendocrine neoplasms (NENs) of the lung encompass neuroendocrine tumors (NETs) composed of typical (TC) and atypical (AC) carcinoids and full-fledged carcinomas (NECs) inclusive of large cell neuroendocrine carcinoma (LCNEC) and small cell carcinoma (SCLC). NETs and NECs are thought to represent distinct and separate lesions with neither molecular overlap nor common developmental continuum. Two perspectives were addressed regarding the morphologic and molecular classification of lung NENs: (i) a supervised approach by browsing the traditional classification, the relevant gene alterations, and their clinical implications; and (ii) an unsupervised approach, by reappraising neoplasms according to risk factors and natural history of disease to construct an interpretation model relied on biological data. We herein emphasize lights and shadows of the current classification of lung NENs and provide an alternative outlook on these tumors focused on what we currently know about the biological determinants and the natural history of disease.

Coexpression of ΔNp63/p40 and TTF1 Within Most of the Same Individual Cells Identifies Life-Threatening NSCLC Featuring Squamous and Glandular Biphenotypic Differentiation: Clinicopathologic Correlations.

Introduction: Double occurrence of TTF1 and ΔNp63/p40 (henceforth, p40) within the same individual cells is exceedingly rare in lung cancer. Little is known on their biological and clinical implications. Methods: Two index cases immunoreactive for both p40 and TTF1 and nine tumors selected from The Cancer Genome Atlas (TCGA) according to the mRNA levels of the two relevant genes entered the study. Results: The two index cases were peripherally located, poorly differentiated, and behaviorally unfavorable carcinomas, which shared widespread p40 and TTF1 decoration within the same individual tumor cells. They also retained SMARCA2 and SMARCA4 expression, while variably stained for p53, cytokeratin 5, and programmed death-ligand 1. A subset of basal cells p40+/TTF1+ could be found in normal distal airways. Biphenotypic glandular and squamous differentiation was unveiled by electron microscopy, along with EGFR, RAD51B, CCND3, or NF1 mutations and IGF1R, MYC, CCND1, or CDK2 copy number variations on next-generation sequencing analysis. The nine tumors from TCGA (0.88% of 1018 tumors) shared the same poor prognosis, clinical presentation, and challenging histology and had activated pathways of enhanced angiogenesis and epithelial-mesenchymal transition. Mutation and copy number variation profiles did not differ from the other TCGA tumors. Conclusions: Double p40+/TTF1+ lung carcinomas are aggressive and likely underrecognized non-small cell carcinomas, whose origin could reside in double-positive distal airway stem-like basal cells through either de novo-basal-like or differentiating cell mechanisms according to a model of epithelial renewal.

Automated Analysis of Proliferating Cells Spatial Organisation Predicts Prognosis in Lung Neuroendocrine Neoplasms.

Lung neuroendocrine neoplasms (lung NENs) are categorised by morphology, defining a classification sometimes unable to reflect ultimate clinical outcome. Subjectivity and poor reproducibility characterise diagnosis and prognosis assessment of all NENs. Here, we propose a machine learning framework for tumour prognosis assessment based on a quantitative, automated and repeatable evaluation of the spatial distribution of cells immunohistochemically positive for the proliferation marker Ki-67, performed on the entire extent of high-resolution whole slide images. Combining features from the fields of graph theory, fractality analysis, stochastic geometry and information theory, we describe the topology of replicating cells and predict prognosis in a histology-independent way. We demonstrate how our approach outperforms the well-recognised prognostic role of Ki-67 Labelling Index on a multi-centre dataset comprising the most controversial lung NENs. Moreover, we show that our system identifies arrangement patterns in the cells positive for Ki-67 that appear independently of tumour subtyping. Strikingly, the subset of these features whose presence is also independent of the value of the Labelling Index and the density of Ki-67-positive cells prove to be especially relevant in discerning prognostic classes. These findings disclose a possible path for the future of grading and classification of NENs.

MEDTEC Students against Coronavirus: Investigating the Role of Hemostatic Genes in the Predisposition to COVID-19 Severity.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic. Besides virus intrinsic characteristics, the host genetic makeup is predicted to account for the extreme clinical heterogeneity of the disease, which is characterized, among other manifestations, by a derangement of hemostasis associated with thromboembolic events. To date, large-scale studies confirmed that genetic predisposition plays a role in COVID-19 severity, pinpointing several susceptibility genes, often characterized by immunologic functions. With these premises, we performed an association study of common variants in 32 hemostatic genes with COVID-19 severity. We investigated 49,845 single-nucleotide polymorphism in a cohort of 332 Italian severe COVID-19 patients and 1668 controls from the general population. The study was conducted engaging a class of students attending the second year of the MEDTEC school (a six-year program, held in collaboration between Humanitas University and the Politecnico of Milan, allowing students to gain an MD in Medicine and a Bachelor's Degree in Biomedical Engineering). Thanks to their willingness to participate in the fight against the pandemic, we evidenced several suggestive hits (p < 0.001), involving the PROC, MTHFR, MTR, ADAMTS13, and THBS2 genes (top signal in PROC: chr2:127192625:G:A, OR = 2.23, 95%CI = 1.50-3.34, p = 8.77 × 10-5). The top signals in PROC, MTHFR, MTR, ADAMTS13 were instrumental for the construction of a polygenic risk score, whose distribution was significantly different between cases and controls (p = 1.62 × 10-8 for difference in median levels). Finally, a meta-analysis performed using data from the Regeneron database confirmed the contribution of the MTHFR variant chr1:11753033:G:A to the predisposition to severe COVID-19 (pooled OR = 1.21, 95%CI = 1.09-1.33, p = 4.34 × 10-14 in the weighted analysis).

Improved label-free LC-MS analysis by wavelet-based noise rejection.

Label-free LC-MS analysis allows determining the differential expression level of proteins in multiple samples, without the use of stable isotopes. This technique is based on the direct comparison of multiple runs, obtained by continuous detection in MS mode. Only differentially expressed peptides are selected for further fragmentation, thus avoiding the bias toward abundant peptides typical of data-dependent tandem MS. The computational framework includes detection, alignment, normalization and matching of peaks across multiple sets, and several software packages are available to address these processing steps. Yet, more care should be taken to improve the quality of the LC-MS maps entering the pipeline, as this parameter severely affects the results of all downstream analyses. In this paper we show how the inclusion of a preprocessing step of background subtraction in a common laboratory pipeline can lead to an enhanced inclusion list of peptides selected for fragmentation and consequently to better protein identification.

All-Trans Retinoic Acid Stimulates Viral Mimicry, Interferon Responses and Antigen Presentation in Breast-Cancer Cells.

All-trans retinoic acid (ATRA), a recognized differentiating agent, has significant potential in the personalized/stratified treatment of breast cancer. The present study reports on the molecular mechanisms underlying the anti-tumor activity of ATRA in breast cancer. The work is based on transcriptomic experiments performed on ATRA-treated breast cancer cell-lines, short-term tissue cultures of patient-derived mammary-tumors and a xenograft model. ATRA upregulates gene networks involved in interferon-responses, immune-modulation and antigen-presentation in retinoid-sensitive cells and tumors characterized by poor immunogenicity. ATRA-dependent upregulation of these gene networks is caused by a viral mimicry process, involving the activation of endogenous retroviruses. ATRA induces a non-canonical type of viral mimicry, which results in increased expression of the IRF1 (Interferon Responsive Factor 1) transcription factor and the DTX3L (Deltex-E3-Ubiquitin-Ligase-3L) downstream effector. Functional knockdown studies indicate that IRF1 and DTX3L are part of a negative feedback loop controlling ATRA-dependent growth inhibition of breast cancer cells. The study is of relevance from a clinical/therapeutic perspective. In fact, ATRA stimulates processes controlling the sensitivity to immuno-modulatory drugs, such as immune-checkpoint-inhibitors. This suggests that ATRA and immunotherapeutic agents represent rational combinations for the personalized treatment of breast cancer. Remarkably, ATRA-sensitivity seems to be relatively high in immune-cold mammary tumors, which are generally resistant to immunotherapy.